The disclosed embodiments of the present invention relate to separating foreground objects from the background, and more particularly, to a region growing method for segmenting an input image (e.g., a depth map or a color image) from unordered pixels into segmented regions with mitigated region chaining.
Depth cameras become popular in recent years in gaming. Detecting and labeling objects (for example, gamers before a depth camera) is an important task which should be done before any further image processing is performed in a computer vision system. When cameras are generally fixed in most cases, objects may be easily separated from the background using background subtraction algorithms. However, background subtraction algorithms may not be able to give perfect separated objects for some frames. For example, due to the possible limitation of background subtraction algorithms, the detected people from depth images may miss parts of a body, such as head, hands or legs. If such incomplete results are fed into later processing stages, the performance may be adversely affected. Before passing these possible incomplete objects to later processing stages, a conventional region growing algorithm may be performed to recover the missing parts.
Region growing is a commonly used technique to segment images from unordered pixels into segmented regions. Conventional region growing methods have been extensively used in both intensity images and range (depth) images. Roughly, the usage of region growing may be divided into two scenarios. One is to segment a whole image into different regions; the other is to segment some objects out of the whole image while leaving the remaining image intact. However, conventional region growing methods suffer from the problem of region chaining (overspill). Region chaining occurs when two regions are grown into one region while they are actually separated from each other.
Region growing was also extensively used in the range image segmentation. However, the main objective of range image segmentation is to label pixels into different surfaces. For most cases, some surfaces are planar, while other surfaces may be curved surfaces. If the object in consideration cannot be represented by those pre-determined surfaces, the segmentation result will not be satisfactory. Besides, region growing used in the range image segmentation is computationally intensive, and cannot separate two surfaces on a same plane joined at edges.
With the introduction of relative cheap depth cameras, image analysis in depth image is becoming popular. For many applications, segmentation of objects is an important step and may be effectively achieved by region growing from seeded regions. Thus, there is a need for an innovative region growing algorithm which is capable of effectively growing regions from seeds without region chaining.